WHO's six laws of AI
That faint shuddering you can feel is Isaac Asimov spinning in his grave.
The latest results by benchmarking consortium MLPerf, tracking the best chips for training the most popular neural networks, are out and a new player has entered the game: Graphcore. Each MLPerf release is pretty standard. A sprawling spreadsheet records the time various systems take to train or run a particular machine- …
> Ensuring equity and inclusivity: AI should not be biased or perform less well against age, sex, gender, income, race, ethnicity, sexual orientation, and so on.
What does this mean? If an algorithm is effective in detecting heart disease risk from pulse oximeter data in people over 30, it shouldn't be allowed because it's less effective on younger people?
I think the idea is more on the line of the "AI" should not disregard any effort in diagnosing possible sickness just because the patient belongs to a particular subset of the human race.
So, basically, "AI" should treat everyone equally and work just hard for each case it is presented with.
Which is an obvious requirement. That said, certain populations may be more at risk of certain types of disease. It might not be that easy to ensure that the AI is not going to neglect any possible signs in populations that are not as much at risk of a specific disease if the markers are present.
You have to read the document itself to understand what it means rather than put your own interpretation on it.
It is quite readable, very wordy, and seemingly written by people who live in ivory towers.
e.g. "Inclusiveness requires that AI used in health care is designed to encourage the widest possible appropriate, equitable use and access, irrespective of age, gender, income, ability or other characteristics. Institutions (e.g. companies, regulatory agencies, health systems) should hire employees from diverse backgrounds, cultures and disciplines to develop, monitor and deploy AI." etc
So, a Mexican for cleaning the office, an Oriental for bringing food and drinks, an African-American for providing drugs and hookers, an Indian for copying and pasting from Stackoverflow, an LGBT woman for HR and a WASP male for managing the whole circus. Gotcha!
Anon, because I'm sick and tired of snowflakes who can't take a joke.